IPCC WG1 FAQ

Reader Michael Smith asked about the provenance of Figure 1.1 in the SPM for the AR4 Synthesis Report. While we’ve had some discussions of WG1, we’ve not discussed the Synthesis Report before. While following up the references for this Figure, I encountered the WG1 FAQ – a document which I had previously not noticed.

I suppose that one of the reasons that I hadn’t noticed the document is that it was never presented to IPCC WG1 reviewers, of which I was one. We were provided copies of the chapters and the Summary for Policy-makers, but the FAQ was not provided to Reviewers. See here for what was given to reviewers. I’ve consulted the WG1 SPM that was approved in Feb 2007 and it contains a reference to the FAQ, so some form existed at the time of the SPM.

It’s a useful and interesting summary. But does anyone know anything about when the FAQ document was released, what caused it to be written, how it was written, who wrote it or what its review process consisted of?

I downloaded the WG1 FAQ (a file called AR4WG1_Pub_FAQs.pdf) on June 1; it’s about 10.9 MB; I can email you a copy. From the front-matter, it states:

These Frequently Asked Questions have been taken directly from the chapters of the underlying report and are collected here. When referencing specific FAQs, please reference the corresponding chapter in the report from whence the FAQ originated.

And indeed, each FAQ (like 3.1 that we’ve been discussing) is also found in the corresponding chapter. FAQ 3.1 is found starting on p. 252 in Chapter 3.

By the way, if the data of Figure 3.6 was the source I believe that means I was right about the meaning of the “uncertainty” in the other thread🙂

This is “sort of” off topic and I am a layperson. I am trying to understand both sides of the global warming debate. The question I have is what are the papers that the IPCC uses to produce their report? In some (online) places, people claim that only AGW papers are considered. Others claim that there are no peer reviewed papers that disagree with AGW. In either case, it’s easy to claim that there is a 100% consensus of AGW. Anyway, I cannot seem to find the answer to this general question. Is there a link or an article that discusses what is included or excluded in the IPCC analysis?

Check out “Frequently Asked Question 8.1 How Reliable Are the Models Used to Make Projections of Future Climate Change?”

The FAQ Ques. 8.1 Summary says: “There is considerable confidence that climate models provide credible quantitative estimates of future climate change, particularly at continental scales and above. This confidence comes from the foundation of the models in accepted physical principles and from their ability to reproduce observed features of current climate and past climate changes.”

However, WGI Chapter 8 Section 8.1.2.2 says: “What does the accuracy of a climate models simulation of past or contemporary climate say about the accuracy of its projections of climate change? This question is just beginning to be addressed … [T]he development of robust metrics is still at an early stage, [so] the model evaluations presented in this chapter are based primarily on experience and physical reasoning, as has been the norm in the past.”

These two WG1 statements cannot be simultaneously true. The WG1 FAQ for public consumption exudes confidence in the physical accuracy of GCM climate projections. Deep in Chapter 8, however, WG1 admit of no physical certainty at all in GCM climate projections.

And in their Section 10.5.4.6, WG1 report that, “[Uncertainty in future temperatures] results from an expert judgement of the multiple lines of evidence presented in Figure 10.29, and assumes that the models approximately capture the range of uncertainties in the carbon cycle.”

This admission of assumptions made and of poor physical understanding flatly contradicts the statement in FAQ 8.1, that: “Models ability to represent these and other important climate features increases our confidence that they represent the essential physical processes important for the simulation of future climate change.(bolding added)”

The “multiple lines of evidence” in Figure 10.29, referenced above to Section 10.5.4.6, merely give ensemble means +/- their SD’s, which transmit nothing of physical uncertainty, while uncertainty in the physics is represented by how much climate variation results when tuning the SCM through different estimated sensitivities. That is, the physical uncertainty is just a range of guesses. This is not at all a quantitative estimate of physical uncertainty. WG1 offers no proper estimate of physical uncertainty anywhere.

So, the FAQ writers’ “confidence … [in] credible quantitative estimates” is nowhere to be found in the WG1 report.

None of the supreme confidence expressed in the FAQ is sustainable in the body of the WG1 report. Just the opposite is true. The authors of the FAQ are quantitatively disavowed by the authors of the WG1 Report. I second Steve M.’s question: Who wrote the FAQ? Who could possibly have reviewed it for accuracy? And I’d add: What planet are the authors living on?

This FAQ continues to invoke a serious fallacy regarding the so-called Butterfly Effect and the original Lorenz system of three ODEs. The last two sentences in the right-hand column just above the Figure 1 say:

A major limiting factor to the predictability of weather beyond several days is a fundamental dynamical property of the atmosphere. In the 1960s, meteorologist Edward Lorenz discovered that very slight differences in initial conditions can produce very different forecast results.

Firstly, note the very clever change in the subject between the two sentences. The first sentence mentions,  a fundamental dynamical property of the atmosphere., while the second sentence mentions,  initial conditions can produce very different forecast results. Secondly, the Butterfly Effect is yet again invoked as a significant physical phenomena.

The first problem should never, and I mean for all times, be a part of any scientific or engineering discussions of technical subjects. A fundamental property of the physical real world is assumed to be determined by use of calculations using software and computers. These two things are not the same. In the case of the original ODEs used by Lorenz, the level of presumption is enormous. Those three simple non-linear ODEs are known and acknowledged to not be a model, at any degree of fidelity, for any fluid flows. They are most certainly not a model of the weather. No fundamental dynamical property of any physical phenomena or processes are encoded in the equations. Additionally, converged, by a practical measure and not a theoretical bases, numerical solutions for the equations are available only for time units less than about 20.

As to The Butterfly Effect, I think it is universally acknowledged that this is not a true property of the physical world. Uncountable numbers of perturbations to the atmosphere are continuously underway. An equally uncountable number of these are far greater in the ranges of both amplitude and frequency than any that might be due to butterfly wings. As an example I offer above ground testing of explosive devices based on fusion. It would seem that such a perturbation would have very long range cascading and teleconnection effects for decades relative to what butterfly wings can do.

Finally, the hypothesis that the weather represents the response of a complex dynamical system is not testable. Equally true is that The Butterfly Effect is also not testable.

10, then they go on to the “heads we win, tails you lose” corollary thusly:

The warmer climate therefore increases risks of both drought − where it is not raining − and floods − where it is − but at different times and/or places. For instance, the summer of 2002 in Europe brought widespread floods but was followed a year later in 2003 by record-breaking heat waves and drought.

I’m sure if another ice age comes, they’ll figure out a way to attribute it.

The concentration of CO2 in the atmosphere has reached a record high relative to more than the past half-million years, and has done so at an exceptionally fast rate. Current global temperatures are warmer than they have ever been during at least the past five centuries, probably even for more than a millennium.

“Observational data at large scale (not individual stations) are used to evaluate the models after theyve been run – but again generally only at the continental scale and above.”

From googling the phrases “continental scales and above” and “continental scale and above,” there are only a few hits – IPCC FAQ, a Sept 2007 italian presentation quoting that FAQ, Anthony’s blog, and two unrelated sites/topics.

The reason the Earths surface is this warm is the presence of greenhouse gases, which act as a partial blanket for the longwave radiation coming from the surface. This blanketing is known as the natural greenhouse effect. The most important greenhouse gases are water vapour and carbon dioxide.

The “most important” GHG are separated by a factor of 24,000 according to Roy Spencer. So how do they both get to be “important”? If I weigh 24,000 pounds and you weigh 1 pound, are we both important to the weight shown on the scale. Also, isn’t an important question, “what effect does CO2 have on climate change?” So including that comment in the answer is begging the question.

About 30% of the sunlight that reaches the top of the atmosphere is reﬂected back to space. Roughly two-thirds of this reﬂectivity is due to clouds and small particles in the atmosphere known as aerosols.

The UK Met:

Table 12 shows the percentage change in sunshine, based on a linear trend starting from 1929. It shows that the greatest and most significant changes occurred in the winter season, when there has been an increase in sunshine of about 20% for central and northern England. Sunshine has also increased in these areas by about 10% in autumn, and by 8% over the year as a whole for eastern and NE England.

Table 10 shows that there has been a strong downward trend in the number of days with
snow cover at 0900 since 1961. The strongest trend has occurred in southern England (Map
9), where there are now about 75% fewer days with snow cover compared to 1961.
Northern England has also experienced a very significant downward trend. The negative
trend is present in all seasons, but is most significant in the autumn period, although
absolute decreases are greatest in the winter, which is the season in which the most snow
occurs.

Considering how important albedo is (snow covered days being an example of a change that would affect albedo), and considering how hours of sunshine would affect temperature if they changed by say … 20%, is there any significant discussion of increased sunshine hours (which is not just limited to the UK) or decreased snow covered days (I have no idea if that is global or just limited to the UK) in the IPCC report?

Newly available surface observations from 1990 to the present, primarily from the Northern Hemisphere, show that the dimming did not persist into the 1990s. Instead, a widespread brightening has been observed since the late 1980s. This reversal is reconcilable with changes in cloudiness and atmospheric transmission and may substantially affect surface climate, the hydrological cycle, glaciers, and ecosystems.

Re #8. On the ABC (Australian Broadcasting Corporation) AM program on 13 October, Andrew Pitman, a lead author of Chapter 8 of the WGI Report, said that “If the people listening to this can imagine what it would be like for every sentence they write to be critiqued by one or two hundred other people, and having to prove that the precise words that one has written can be defended via the scientific literature against all those critical views that people might put forth, they might get a sense of how rigorous those reports are.” This comment raises the question whether the precise words in every sentence in Frequently Asked Question 8.1 were critiqued by one or two hundred other people and defended by the authors – and, if the answer is “yes”, who were those authors and who were the 100-200 other people who critiqued their work and agreed that “There is considerable confidence that climate models provide credible quantitative evidence of future climate change …”?

#22 — From what I’ve read of it, WG1 Chapter 8 of the 4AR seems soundly written to me. It’s candid about model limitations and errors, with even more admissions in Chapter 8 Supplemental. So, I haven’t a problem with Dr. Pitman on that score. Where I’d criticize him is in defending the IPCC conclusions of AGW, following a disjointed process that somehow began with his rather honest Chapter 8 and unaccountably ended up with the specious claims of the SPM.

That is, the disconnect comes in the SPM, and now in the FAQ, in which the errors and uncertainties admitted in Ch. 8 make no appearance at all. For that matter, the WG1 Technical Summary isn’t forthright about uncertainties, either.

Instead, in the SPM, we get numerical SD’s speciously represented as though they were physical uncertainties and, in the FAQ, false assurances of high confidence in the physical reliability of the models.

Steve M. doesn’t like it when I am candid on this blog about which 3-letter word starkly describes such activities. But the word is simple to guess in light of claims knowingly made that are baldly inconsistent with what is obviously true.

With respect to who had a hand in writing the FAQ, I offer this statement, again from FAQ Section 8.1: “One source of confidence in models comes from the fact that model fundamentals are based on established physical laws, such as conservation of mass, energy and momentum…”

Compare that statement with this: “And, a [GCM] computer model is nothing more than an embodiment of 200 years of independently tested pieces of the physical theory .,” which you can find in response to comment #193 here at RC.

Margo strongly criticized that author for so overselling GCMs here, but those two representations of climate models seem to embody the same portentous approach to overselling GCMs in terms of 19th century physics that are necessary but far from sufficient to credibly support a claim of physical accuracy.

The sunshine figures have not just been measured by Campbell-Stokes instruments, in recent years they have been changed to a Kipp and Zonen type, the figures you quote are all normalised by a conversion factor to Campbell Stokes equivalent
The conversion factor is still being improved, if you ask the nice UK Met office man who wrote the report he will tell you how he does it, he seems to know what he is about, and is quite refreshingly open to questions from nerds like you and me, but please don’t ask him about global warming just ask about his statistical report, because that is all he has produced.

If I have understood the reports (WG1 FAQ) the IPCC would probably say that extra sunshine (less clouds) may be caused by increase in Temp. Of course there are lots of other things that would cause that, most of which I don’t understand.

For UK that doesn’t really ring true, as winter mean is unchanged, but we do get more sunshine, almost no fog days etc. when compared to early part of 20th century, so things have changed, but they aren’t unusual.

#10 Dan Hughes draws attention to the serious fallacy regarding the so-called Butterfly Effect. IPCCs: FAQ 1.2 What is the relationship between Climate Change and Weather, demonstrates an even more naive assertion by the analogy: while it is impossible to predict the age at which any particular man will die, we can say with high confidence that the average age of death for men in industrialised countries is about 75. Can a statistic die? More on: http://www.whatisclimate.com/index.html ; Subject: B-205c – IPCC WG I attempt to explain: What is the relationship between Climate Change and Weather? Successfully?

Table 10 shows that there has been a strong downward trend in the number of days with
snow cover at 0900 since 1961. The strongest trend has occurred in southern England (Map
9), where there are now about 75% fewer days with snow cover compared to 1961.
Northern England has also experienced a very significant downward trend. The negative
trend is present in all seasons, but is most significant in the autumn period, although
absolute decreases are greatest in the winter, which is the season in which the most snow
occurs.

That has just got to be an unbelievable example of propaganda masquerading as science. I’m old enough to remember the winter of 62/63 (just OK). In souther england it snowed on Boxing Day and the snow stayed on the ground until March. it was an exceptional event – the Thames froze (albeit briefly and you couldn’t have held a fair on it). The sea froze at Herne Bay.

OK this may not seem unusual to canadians – but it was a very exceptional event in England.

That statement is so bogus though not as bad as the graph in the PDF cited. If you took the trend from say 1914 it would be rather different. If you made the smoothing period 5/7/9/11 years it would look very different. They only show the smoothed results over 25 years – not the raw data – so it’s hard to tell how bogus it really is. It looks like a steady decrease over the decades – complete nonsense. I’d download the data and draw the graph myself – but you have to register as a bonafide researcher before they’ll let you have it.

Computer Modelling is not going to give any other answer than what the person/persons who wrote the program wanted it to give. This is a fundamental problem of all Climate Models.

The Academic Science community and politicians know little about computers, they did not even understand that the Y2K computer bug was a Myth, they remained silent or worse, even though everything to do with the problem was “man made”. This is a fundamental problem of the Science/Politics community

Observational records depend on interpretation, and must be adjusted for any sort of historical comparison to be made.Adjustments are made according to rules laid down by people. This is a fundamental problem of all observational records.

Proxy climate reconstructions are totally constructed by people. This is a fundamental problem of all Proxy records.

History was written by people. This is a fundamental problem of history, but at least they were there.

I think this FAQ will become a standard courseware in high schools and undergraduate levels, because it is skillfully illustrated and written in a very readable style. In fact, it is dangerous stuff, as uncertainties are not mentioned, and inconvenient facts either overlooked or handled in a very discrete and non obstrusive manner. A prime example are methane concentrations. Page 8 is the sole place telling that “methane concentrations are not currently increasing”. It would have be more correct to insist that methane levels are nearly stable since 9 years, that CH4 growth rate is currently negative, that most IPCC scenarios do not take not of this and that most IPCC graphs conveniently leave out this last 9 years plateau.

Chapter 2 of the Working Group I report discusses the derivation of this forcing and its uncertainty interval. Figure 2.14 ( see here: http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter2.pdf ) shows the outputs of 28 model runs. The text says the values for Cloud Albedo effect in these model runs ranges from -.22 to -1.85 W/m-2.

Then, in section 2.4.5.5, titled Uncertainties due to Model Biases, the author gives a very frank assessment of numerous model deficiencies that create uncertainty about their results. Regarding the models, Major uncertainties remain, according to the text.

This is followed by section 2.4.5.6, which tells us that even though major uncertainties remain, the models allow them to now state that the radiative forcing for Aerosol Cloud Albedo effect is 0.7 W m2 as the median, with a 5 to 95% range of 0.3 to 1.8 W m2.

They conclude by noting that the scientific level of understanding of cloud albedo effect has improved to low.

Note that the range of the uncertainty interval is (slightly) less than the range of values produced by the models.

So here is my question: How can the models be the basis of an estimated uncertainty interval when there are so many uncertainties about the models themselves?

Apparently, their assumption is that the models have bracketed reality — that is, that the models producing the high values (such as -1.85) are overstating the effect while the models producing low values are understating the effect. Given a stated level of scientific understanding of low, how can one have any confidence in this assumption?

To me, it simply isn’t meaningful to try and put a trend line through snow in Southern England. Many years there won’t be any snow. If there were years of negative snow fall it might not be so bad. Then right at the start of the period being investigated you have the coldest winter for 223 years. (discussed on the Met office site at: http://www.metoffice.gov.uk/education/secondary/students/winter.html. We brits do like talking about the weather.

I wasn’t going to look at how the numbers were put together. I didn’t think it mattered and anyway I’m no statistician. But then I went and had a look. And it just seems to get weirder.

On the first page the document says that it hasn’t been published. Which is odd as it appears to be about 18 months old, they’ve posted it on the web-site and it’s marked as Crown Copyright.

The next thing to bear in mind, is that this isn’t based on real data. It’s based on gridded data. These statistics are derived not from the raw data but from an artificial dataset. Most of the figures in this artificial dataset have been obtained by interpolation between different real data values. These figures have then been averaged together for each region. It’s nice for producing pretty maps. And handy if you want to check your regional computer model I guess. But it’s hard to know what kind of biases might have been introduced by such a process.

Next off, the way the data series are smoothed. I think I said a 25 year average – wrong – that was from my quick skim. It’s described as a triangular kernel filter with 14 years either side. So I guess you take the main value + 13/14ths of the years each side, then 12/14ths then 11/14ths and so on. Then you divide the whole thing by whatever is required. That’s going to seriously smooth out any wrinkles.

Based on the description in Section 3 of the document, this appears to be applied first, then the resultant data is tested to see if it has a linear trend. This may not be what happens but that seems to be how it’s described. Wouldn’t something like a least squares fit to the original data be a more legitimate approach? I may not understand this, but it seems like they’ve taken a bit of corrugated iron and said: ‘is it flat?’, ‘Dunno can’t tell there are all these damned wrinkles’. So they stick it in a 20,000 Tonne press to, well, iron out those wrinkles. ‘Now is it flat?’

But wait there is more. End-points. They’ve plotted the data from 1961 to 2005. I assumed that the 1961 data would presumably include data from the previous 14 years (even though not plotted), I was wrong. And how do you apply a 14+1+14 year filter to the year 2005? So they simply don’t bother. The end points are smoothed only by the data inside the range. I think that will have the effect of making the start of the line higher than it would otherwise be (as 1963 was the coldest winter for 223 years) and because the last few years have been fairly mild, the end of the graph is probably lower than it ought to be – or might well turn out to be once we have the next 14 years worth of data. Really they should have drawn an error region at the ends to show that they could be out. Though in the case of the 1963 value you’d think they could have found some data for the previous 14 years.

However, perhaps I shouldn’t be complaining. This document isn’t setting out to prove that global warming is happening. That’s taken as a done deal. They’ve been charged with estimating the impact of global warming on the UK. So they have to believe there is a trend even if there isn’t otherwise how can they make a prediction? Hopefully no one will take any notice of this – but it’s always possible the Highways Agency might decide it no longer needs any snow plows. And then we will be stuffed if we have another 1963 style winter.

The other danger, is that these derived trends get reported as if they were evidence of global warming:
‘average snowfall in southern England has dropped by 35% since 1961’. But to do that would be a somewhat circular argument.

Re #8 and #24. Pat Frank, Question 8.1 WAS included in the drafts of Chapter 8 that were sent to reviewers, and was presumably produced by the same writing team as the body of the Chapter. In the First Order Draft the Question was in the body of the text (pps. 8-12 and 8-13) and in the Second Order Draft it was placed at the end of the Chapter (but before the tables and figures). In the final version, FAQ 8.1 was again placed in the body of the text.

Contrary to the impression given by Professor Pitman in the ABC interview that I quoted, there were some significant changes in the final version which appear to have been made without the authors “having to prove [to one or two hundred other people] that the precise words that [they had] written can be defended via the scientific literature.” For example, Question 8.1 in the First and Second Order Drafts referred (final para.) to “considerable confidence that [models] are able to provide USEFUL PROJECTIONS of MANY ASPECTS OF future climate change” (EMPHASES added). In the final version the statement was strengthened, so as to refer to “considerable confidence that [models] are able to provide CREDIBLE QUANTITATIVE ESTIMATES of future climate change” (EMPHASIS added, and note that the reference to “many aspects” was deleted).

The First Order Draft of Question 8.1 in Chapter 8 included the statement that models “can also reproduce features such as the reduction in the diurnal temperature range”, and the corresponding passage in the Second Order Draft said that “Models also reproduce other observed features, such as the reduction in the diurnal temperature range [DTR]…” The final version removed the technical term but was otherwise unchanged: “Models also reproduce other observed changes, such as the faster increase in nighttime than in daytime temperatures” (p. 601).

But it turns out that there has NOT been a faster increase in nighttime than in daytime temperatures, and the AR4 SPM specifically says so (“Updated observations reveal that DTR has not changed from 1979 to 2004 as both day- and night-time temperature have risen at about the same rate” page 9).

The authors of Chapter 8 were presumably relying on earlier IPCC Assessment Reports. For example, the TAR (2001) said that “On average, between 1950 and 1993, night-time daily minimum air temperatures over land increased [at] about twice the rate of increase in daytime daily maximum air temperatures…” (SPM, p. 2, last dot point). According to Table 1 on p. 15 of the TAR SPM, the confidence levels attached to both the observed reduction in the DTR over most land areas in the latter half of the twentieth century, and in the projected changes in this phenomenon in the 21st century, were “very likely”.

Now that it is recognised that the DTR did not change between 1979 and 2004, these confident assessments can no longer be sustained: there is no reference to the phenomenon in Table SPM-2 (p. 8) in the SPM of the AR4. If models show a faster increase in nighttime temperatures in the last decades of the twentieth century, as stated in Chapter 8, the authors confidence in the models should have been weakened (whereas the main theme of the Chapter is that confidence in the models has increased since the TAR).

As long ago as 1996 Professor David Karoly (subsequently a lead author of the TAR and AR4) included “reduction in the diurnal temperature range” among the pieces of evidence that had led the IPCC to reach the conclusion that “the balance of evidence suggests a human influence on climate” (“Detecting a Human Influence on Climate” in Australian National Academies Forum conference “Australians and Our Changing Climate: Past Experience and Future Destiny”, 25 November 1996, Summary of Proceedings, p. 39).

The first sentence of the Easterling et al paper stated that “Analysis of the global mean surface air temperature has shown that its increase is due, at least in part, to differential changes in daily maximum and minimum temperatures, resulting in a narrowing of the diurnal temperature range (DTR)”, and the penultimate paragraph concluded with the statement that “[I]t is likely that the maximum and minimum temperature and DTR changes presented here continued to occur through 1995.”

Two of the authors of Easterling et al (Karl and Folland) were the Coordinating Lead Authors of Chapter 2 of the TAR, and another author of the paper published in Science (Salinger) was one of the eight Lead Authors of the same Chapter – the Executive Summary of which includes the following:

“Analyses of mean daily maximum and minimum land surface air temperatures continue to support a reduction in the diurnal temperature range in many parts of the world with, globally, minimum temperatures increasing at nearly twice the rate of maximum temperatures between about 1950 and 1993. The rate of temperaturre increase during this time has been 0.1 C and 0.2 C for the maximum and minimum, respectively” (p. 101).

The body of the chapter identifies Easterling et al as the source of these statements (p. 108).

Subsequently, following the identification of serious errors in the Easterling et al study, the NOAA press release, and the paper itself, disappeared from the NOAA website. The text of the press release remains available on “The Heat is Online” website. To the best of my knowledge, no correction of the Easterling et al paper has ever been made by Science, notwithstanding the fact that the paper was deeply flawed in methods, results and conclusions.

“The 1980s were a particularly snowy period for the UK, as well as the start of the
1960s, with 1962/63 being the snowiest season for England and Wales. The decreases are
weaker but still significant for most districts when starting from 1963/64, to avoid skewness
introduced by the high values of 1962/63. The trend is weaker for Scotland and Northern
Ireland, where the snowy period in the 1980s was especially significant, and the early
1960s snow less so (Figure 13). “

OK Steve, but at least one part of what I wrote was on-topic. You said in your original post that the FAQs weren’t presented to IPCC WGI reviewers. But for FAQ 8.1 (at least) that question was presented for review – twice.
Steve: ?? what did I move that I shouldn’t have? If you point, I’ll re-edit.

Re Frequently asked question 1.2: What is the relationship between climate change and weather?

The authors answer the question by confusing climatology with predictable climate change and attempting to differentiate it from the uncertainties of weather prediction. They infer that studying and measuring climate gives them the power to accurately predict its behavior in the future based on trends. They use some poor analogies and examples to make their point.

The first, which attempts to distinguish between weather and climate:

As an analogy, while it is impossible to predict the age at which any particular man will die, we can say with high confidence that the average age of death for men in industrialized countries is about 75.

First, mortality in men has a distinct beginning and an end. Human beings will be born and they will die. The dates of each are usually recorded accurately. Weather and climate, although they can be observed, measured and predicted, have no beginning or end, other than the time constraints provided by studies of trends.

We’ve been studying the relatively simple process of human mortality for a long time and have mostly unassailable data to rely on. Can the same be said of the extremely complex global climate system?

They go on and say:

More precisely, climate can be viewed as concerning the status of the entire Earth system, including
the atmosphere, land, oceans, snow, ice and living things (see Figure 1) that serve as the global background conditions that determine weather patterns. An example of this would be an El Niño affecting the weather in coastal Peru. The El Niño sets limits on the probable evolution of weather patterns that random effects can produce.

But isn’t el Nino a somewhat mysterious phenomenon? From what I understand, they don’t know what causes it, they don’t know how long it’s going to last, and they can’t predict when the next one will occur. Sounds suspiciously more like weather than climate to me. Can they accurately predict the years El Nino will occur a half century from now?

Then one last simplistic analogy:

As an example, while we cannot predict the outcome of a single coin toss or roll of the dice, we can predict the statistical behaviour of a large number of such trials.

Suffice it to say, there are only two possible outcomes in a coin toss. Climate, on the other hand, is infinitely variable.

From these weak analogies the authors confidently deduce the following:

Projecting changes in climate (i.e., long-term average weather) due to changes in atmospheric composition or other factors is a very different and much more manageable issue.

and

Projecting changes in climate due to changes in greenhouse gases
50 years from now is a very different and much more easily solved problem than forecasting weather patterns just weeks from now. To put it another way, long-term variations brought about by changes in the composition of the atmosphere are much more predictable than individual weather events.

and

As climate changes, the probabilities of certain types of weather events are affected. For example, as Earths average temperature has increased, some weather phenomena have become more frequent and intense (e.g., heat waves and heavy downpours), while others have become less frequent and intense (e.g., extreme cold events).

All of which brings to mind another coin-toss analogy regularly used on this blog:

But isnt el Nino a somewhat mysterious phenomenon? From what I understand, they dont know what causes it, they dont know how long its going to last, and they cant predict when the next one will occur. Sounds suspiciously more like weather than climate to me. Can they accurately predict the years El Nino will occur a half century from now?

These are useful in giving conditional predictions (disclose the condition post hoc)

Professor Phil Jones, Director of UEA’s Climatic Research Unit, said, “The year began with a weak El Niño  the warmer relation of La Niña  and global temperatures well above the long-term average. However, since the end of April the La Niña event has taken some of the heat out of what could have been an even warmer year.”

lucia: Here’s what they have to say about the science that allows them to predict the roll of the dice:

While many factors continue to influence climate, scientists have determined that human activities have become a dominant force, and are responsible for most of the warming observed over the past 50 years. Human-caused climate change has resulted primarily
from changes in the amounts of greenhouse gases in the atmosphere, but also from changes in small particles (aerosols), as well as from changes in land use, for example.

They’ve “determined” the main cause of global warming– it’s mostly anthropogenic– and now they can use the models to predict what the future may bring.

At the start of 2007 they worked out that 2007 was going to be the warmest year ever a 70% chance they said, and before the year was over with one month to go? they now say it wasn’t, but it was still warm.

They also say a misleading thing about Antartic Sea Ice extent which was at a record winter Max in Oct 07, they say “near average for most of the time” but at this moment it is 2 million sq km above the average which is just as significant as anything the Artic went below this summer.

The total global sea ice extent across both hemispheres has hardly altered since 1979, I don’t know why this is happening and I am now convinced they know as much as me!

Thanks Steve. According to the cover page of the “”Frequently Asked Questions” document within the WGI Report, the FAQs were “taken directly from the chapters of the underlying report”. On my reading, each FAQ was written by the authors of the chapter in which it appeared and was sent to reviewers when the First and Second Order Draft chapters were circulated. Reviewer comments and IPCC authors’ responses, including on the FAQs, have now been published. I don’t think that Pat Frank is correct in saying that the FAQ writers confidence [in] ‘credible quantitative estimates’ is nowhere to be found in the WG1 report” (#8): the FAQs are an integral part of the WGI report.

In response to a comment (No. 8-708) from Dan Hughes (author of #9 above) that a statement made in the Second Order Draft of Question 8.1 was “not entirely correct” and “severely incomplete on several significant levels”, the authors of Chapter 8 said that “The aim of the FAQ’s is not to serve as a textbook on climate modelling …” Maybe so, but it is disturbing to find FAQ 8.1 asserting that “Models also reproduce other observed changes, such as the faster increase in nighttime than in daytime temperatures”, which conflicts with the recognition in Chapter 3 of the WGI report that there was no trend in the diurnal temperature range (DTR) between 1979 and 2004 (pps. 243, 251).

# 26 & #41: FAQ : The El Niño sets limits on the probable evolution of weather patterns that random effects can produce. #41 But isnt el Nino a somewhat mysterious phenomenon?http://www.whatisclimate.com suggest to look and understand the ocean, and you will know when it is El Niño-time again. Few years ago Dake Chen et al. (NATURE, 2004, p. 733ff) concluded that the motion of the warm water pool is causing changes in the atmosphere and not vice versa. Leonardo da Vinci (1452-1519) regarded: Water as the driver of Nature. http://www.whatisclimate.com/ says: Climate is the continuation of the ocean by other means as mentioned in : Letter to NATURE, 1992, Vol.360, p.292.

#36 — Ian you were right. I went back and looked at WG1 Chapter 8. On page 600 it includes a QA that is word-for-word identical to FAQ question 8.1. From the title, through the text, to the Figure plus its caption, the WG1 Chapter 8 question and answer is identical to FAQ 8.1. In WG1 Chapter 8, it’s even called “Frequently Asked Question 8.1.” So, I take back the first three sentences of my #24, and the “rather honest” phrase of sentence 4.

I guess I was so focussed on the model errors that are discussed, especially in WG1 Chapter 8 Supplemental, that the rest had slipped my mind.

If Andrew Pitman went over WG1 Chapter 8 line-by-line so that, “the precise words that one has written can be defended via the scientific literature,” then there is no way to avoid concluding that the insupportable assurances of WG1 Chapter 8 FAQ 8.1 and its clone in the FAQ document were consciously composed to mislead by Dr. Pitman and his colleagues.

#47 — Ian, you wrote, “I dont think that Pat Frank is correct in saying that the FAQ writers confidence [in] credible quantitative estimates is nowhere to be found in the WG1 report (#8): the FAQs are an integral part of the WGI report.”

What I meant by that, Ian, is that the confidence in climate models expressed by the WG1 FAQ authors is not sustained by the physical modeling errors that are documented in WG1 Chapter 8, especially Chapter 8 Supplemental. I didn’t mean to imply that the authors themselves didn’t write that they had such confidence. Sorry for being unclear.

Thanks Pat. It’s relevant to note that Chapter 8 of WGI was the main subject of the study “Global Warming Forecasts by Scientists Versus Scientific Forecasts” by Kesten Green of Australia’s Monash University and Scott Armstrong of the Wharton School, University of Pennsylvania (Energy & Environment, 2007, vol. 18, nos. 7 & 8). Dr Green and Professor Armstrong examined the Chapter to assess the extent to which the IPCC authors complied with forecasting principles”. They “found enough information to make judgments on 89 out of a total of 140 forecasting principles” and concluded that “The forecasting procedures that were described violated 72 principles” and that “Many of the violations were, by themselves, critical.”